{"id":"W4245720599","doi":"10.36227/techrxiv.16973650","title":"Random Fourier Feature Based Deep Learning for Wireless Communications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Kernel (algebra); Fourier transform; Wireless; Computer science; Artificial intelligence; Convergence (economics); Feature (linguistics); Kernel method; Algorithm; Pattern recognition (psychology); Mathematics; Telecommunications; Support vector machine; Discrete mathematics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006678862,0.0005213358,0.0004349755,0.0003765914,0.0001419501,0.0006392389,0.0007279252,0.0006997419,0.002310393],"category_scores_gemma":[0.002595212,0.0002075769,0.0002685681,0.0006517486,0.000457674,0.001272937,0.0006944984,0.001305892,0.0006616936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000668437,"about_ca_system_score_gemma":0.0004552109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001763951,"about_ca_topic_score_gemma":0.001866297,"domain_scores_codex":[0.9997916,0.0000569223,0.000008950707,0.00003332137,0.00007482359,0.00003447005],"domain_scores_gemma":[0.999518,0.0002912389,0.00003986658,0.00005846109,0.00007776877,0.00001456985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001717199,0.0001007723,0.0009451423,0.0002332682,0.00007133978,0.00015615,0.00005130176,0.5252517,0.01014598,0.1594445,0.007915387,0.2955129],"study_design_scores_gemma":[0.000002710049,0.0000138322,0.0001188179,0.000007405623,0.000004144499,0.00002699537,0.000003611065,0.9814626,0.001287629,0.01582016,0.001248139,0.000003954754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009927584,0.0008596467,0.9861151,0.0003608944,0.00006080506,0.00001195909,0.00009346905,0.0003733062,0.002197235],"genre_scores_gemma":[0.7556155,0.002407955,0.2236906,0.0003466072,0.0002095322,0.0001025362,0.0004892143,0.0002114926,0.01692666],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002310393,"threshold_uncertainty_score":0.007729053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02524193663976559,"score_gpt":0.2820052568562753,"score_spread":0.2567633202165097,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}